Data Mining of High Density Genomic Variant Data for Prediction of Alzheimer

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Data mining of high density genomic variant data for prediction of Alzheimer'sdisease risk.

BMC Medical Genetics 2012, 13:7 doi:10.1186/1471-2350-13-7

Natalia Briones ([email protected])Valentin Dinu ([email protected])

ISSN 1471-2350

Article type Research article

Submission date 22 July 2011

Acceptance date 25 January 2012

Publication date 25 January 2012

Article URL http://www.biomedcentral.com/1471-2350/13/7

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Data mining of high density genomic variant data for

prediction of Alzheimer's disease risk

Natalia Briones1, Valentin Dinu

2

1Computational Biosciences Program, School of Mathematics and Statistical

Sciences, Arizona State University, 1711 South Rural Road, Tempe, Arizona, 85287-

1804, USA

2Department of Biomedical Informatics, Arizona State University,

Mayo Clinic, Samuel C. Johnson Research Bldg. 13212 East Shea Boulevard

Scottsdale, Arizona 85259,

USA

Corresponding author

Email addresses:

NB: [email protected]

VD: [email protected]


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Abstract

Background

The discovery of genetic associations is an important factor in the understanding of

human illness to derive disease pathways. Identifying multiple interacting genetic

mutations associated with disease remains challenging in studying the etiology of

complex diseases. And although recently new single nucleotide polymorphisms

(SNPs) at genes implicated in immune response, cholesterol/lipid metabolism, and

cell membrane processes have been confirmed by genome-wide association studies

(GWAS) to be associated with late-onset Alzheimer's disease (LOAD), a percentage

of AD heritability continues to be unexplained. We try to find other genetic variants

that may influence LOAD risk utilizing data mining methods.

Methods

Two different approaches were devised to select SNPs associated with LOAD in a

publicly available GWAS data set consisting of three cohorts. In both approaches,

single-locus analysis (logistic regression) was conducted to filter the data with a less

conservative p-value than the Bonferroni threshold; this resulted in a subset of SNPs

used next in multi-locus analysis (random forest (RF)). In the second approach, we

took into account prior biological knowledge, and performed sample stratification and

linkage disequilibrium (LD) in addition to logistic regression analysis to preselect loci

to input into the RF classifier construction step.

Results

The first approach gave 199 SNPs mostly associated with genes in calcium signaling,

cell adhesion, endocytosis, immune response, and synaptic function. These SNPs

together withAPOE and GAB2 SNPs formed a predictive subset for LOAD status

with an average error of 9.8 % using 10-fold cross validation (CV) in RF modeling.


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Nineteen variants in LD with ST5, TRPC1, ATG10, ANO3, NDUFA12, and NISCH

respectively, genes linked directly or indirectly with neurobiology, were identified

with the second approach. These variants were part of a model that includedAPOE

and GAB2 SNPs to predict LOAD risk which produced a 10-fold CV average error of

17.5 % in the classification modeling.

Conclusions

With the two proposed approaches, we identified a large subset of SNPs in genes

mostly clustered around specific pathways/functions and a smaller set of SNPs, within

or in proximity to five genes not previously reported, that may be relevant for the

prediction/understanding of AD.

Keywords

Late-Onset Alzheimers Disease, GWAS, SNPs, Random Forest

BackgroundIt is predicted the number of people who suffer from Alzheimers disease (AD) will

increase from 5 million to 13.4 million in the United States of America and will be

115.4 million worldwide by 2050 [1, 2]. There is currently no treatment to stop or

reverse the progress of this disease. This neurodegenerative disorder is believed to be

caused by an inability to clear -amyloid (increasing all its forms: monomer,

oligomer, insoluble fibrils, and plaques) from the Central Nervous System provoking

neuronal impairment and cell death, and by tangled tau formation when cells are

dying [3]. Genetic variation is an important contributor to the risk for this disease,

estimated to be up to seventy-nine percent in the late-onset AD (LOAD) more

frequent form of the disease [4]. A few genes have been confirmed by independent

studies to be implicated with LOAD, summarized below.


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Alzheimers can be divided into early-onset AD (EOAD) and LOAD. There

are thus far three established genes involved in EOAD and follow autosomal

dominant inheritanceAPP (-amyloid precursor protein), PSEN1 and PSEN2

(presenilin-dependent -secretase activity cuts amyloid precursor proteins into -

amyloid peptides) [5, 6]. Another well established genetic risk factoris APOE(it

encodes a lipoprotein that may interact with accumulated -amyloid); it manifests in

the more common LOAD and its inheritance does not follow Mendelian principles [7,

8]. APOEhas three common alleles, 4, 3, and 2, and each of these variants of the

gene are determined by two single nucleotide polymorphisms (SNPs). In European

populations, 44 homozygotes are the most likely to develop disease, followed by

34 heterozygotes and 33 homozygotes, with 2 heterozygotes having the least risk

[8, 9]. However, a person who has one or two copies of4 may never develop AD,

while another who does not carry the 4 alleles may [8].

APOEgenotypes could be useful in combination with other genetic variations

to predict disease risk since the scientific literature suggests the existence of

additional genetic factors associated with LOAD. In the past two years, at least eight

genes mapped to the immune system, cholesterol metabolism, and cell membrane

processes have been confirmed by independent genome-wide association studies

(GWAS) to be implicated with LOAD (See AlzGene database [10]). The genetic

factors are CLU(it encodes apoliprotein J and may have a similar function as to that

ofAPOE), PICALM(it encodes a protein involved in intracellular traffic of

neurotransmitters between proteins and lipids), CR1 (it encodes the main receptor of

complement C3b protein thought to be involved in -amyloid clearance through

phagocytosis) [5, 11, 12],BIN1 (it is involved in synaptic vesicle endocytocis) [5, 13,

14]; moreover, recently two separate studies conducted by Hollingworth P., et al and


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Naj, A.C. et al identifiedMS4A6A/MS4A4E(these encode cell membrane proteins),

CD2AP (encodes a protein involved in endocytocis),EPHA1 (it produces a membrane

bound protein involved in cell and axon guidance and synaptic function; additionally,

it is involved in cell morphology, motility, and inflammation), and provided further

support for CD33 (it is involved in cell-cell interaction and function regulation of

cells in the immune system and also mediates endocytocis through a process

independent from clathrin) [14, 15]. Different SNPs in CD33 were previously

identified by Bertram, L. et al [14-16]. CLU, PICALM, CR1 andBIN1 were

confirmed by Naj, A.C. et al andBIN1 and CR1 were confirmed by Hollingworth P.,

et al as LOAD susceptibility loci [14, 15]. In the study by Naj, A.C. et al, the genetic

effect for the most salient SNPs at each locus had estimated population attributable

fractions (PAF) of 2.72 % - 5.97 %; nonetheless, the authors caution that the true PAF

might be different [15]. These newly confirmed genes could be mapped to pathways

related to the innate and adaptive immune response (CLU, CR1, CD33, EPHA1)

[14, 17], cell membrane processes including endocytocis (PICALM, BIN1, CD33,

CD2AP) [14], and cholesterol/lipid metabolism (CLU) [14, 17].

A few years ago, another gene that was shown to have an increased associated

risk with LOAD was GAB2 although with inconsistent reproducibility by independent

GWAS [18, 19]. GAB2 protein may be involved in protection from the formation of

insoluble tau deposits known as neurofibrillary tangles (NFTs) [9] and may

participate in the production of-amyloid [20]. Reiman, Eric. M. et al utilized

stratification and linkage disequilibrium (LD) analysis and found six SNPs, part of a

common haplotype block covering the GAB2 gene, to have a strong interaction with

APOEin three groups ofAPOE4 carriers [9].


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APOEby itself, or in combination with GAB2, remains to some extend a weak

predictor for the risk of developing AD [8]. We used a published GWAS data set

from Reiman and colleagues [9] to analyze it for AD risk determination in new loci

by different models inAPOE4 positive and negative samples.

One of the challenges trying to identify multiple interacting genetic

mutations associated with disease in studying the etiology of complex diseases arises

from the fact that there are millions of genome-wide variants, many of them untyped

in the study samples of GWAS, and the number of possible combinations encountered

in interaction analysis grows exponentially with the number of variants. As a

result, it is computationally prohibitive to perform a comprehensive test for

interaction analysis between four or more factors and disease. Heuristic approaches

must be developed to analyze these data, that leverage and combined statistical and

data mining methods. [21].

We devised two informatics approaches to identify new genetic biomarkers.

The first approach utilizes statistical and data mining methods. The second approach

also leverages prior biological knowledge to refine the analysis. In both approaches,

multi-locus (classifier building) analysis is done with a reduced number of variants

that first passed, for instance, a single-locus (logistic regression) threshold.

Results and discussion

Approach I: Choice of SNPs without prior biological knowledge for modelbuilding

Step 1

In order to cast a wide net to filter the data and take into account the correlation

among some of the SNPs due to LD, the association analysis was run with a p-value =

1E-3. SNPs fromAPOEand GAB2 were excluded from the analysis, since these are


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already known to be associated with AD in this data set [9]. This gave 199 SNPs with

p-values < 1E-3 and 1 < ORs < 5. Table 1 lists the top seven scoring SNPs; the

complete list of all 199 SNPs is found in additional file 1, table SA.

Step 2

After univariate association analysis, the Random Forest (RF) classifier performance

assessment was done with the 199 SNPs data. With 100 trees, Figure 1 shows the test

and out of bag (OOB) errors for different number of features (SNPs). The figure

suggests that increasing the number of attributes above 70 actually leads to a gradual

increase in test error rate, 10-fold cross validation (CV), for the 199 SNPs andAPOE

SNP and the 199 SNPs,APOESNP and GAB2 SNPs sets. OOB error rate (estimated

class i is determined from models where row i is out-of- bag) is not a good

estimation of test error in all instances here; however, as features are added to the

forest the OOB error becomes a better estimator of the test error for these two data

sets. Figure 2 shows the classifier tuning; the additional induced randomness on the

selection of number of attributes for choosing the splits seems to have worked, giving

a modest improvement with average 10-fold CV error rates in the range of 23-27 %.

In order to further improve the classifiers, a supervised instance resample filter

was applied to the data. The original case-control distribution in the data is 61 %

cases and 39 % controls. After the data is filtered the distribution of the data becomes

52 % cases and 48 % controls. A big reduction in misclassification was obtained by

first resampling the data to make its distribution more balanced followed by RF. The

results at 100, 300, and 600 trees and various numbers of attributes are listed in Table

2. WhenAPOEand GAB2 SNPs alone are used, the average classification error rate

is 33.3 %. This error rate is reduced when the 199 SNPs are used for classification.

Random forests built with eighteen SNPs from the 199 SNPs give an average 10-fold


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CV error of 11.7 %, and when the 199 SNPs are added to the bag containing either an

APOESNP or anAPOESNP and GAB2 SNPs the average 10-fold CV error is

reduced to between 9.3 % and 9.5 % for 11 and 18 attributes respectively.

A manual compilation of pathway, disease association, or biological function

information reveals some of the 199 SNPs are associated with genes involved in

calcium signalling, cell adhesion, endocytosis, and immune response in addition to

synaptic function. This information was added to both Table 1 and the additional file

1, table SA. Some of the genes linked to the 199 SNPs appear among genes

previously identified in GWAS as posted in the AlzGene database [10]. Furthermore,

some of the top 199 SNPs are novel SNPs part of or in proximity (may be acting as

flags) to genes that may be engaged in a cascade of events leading to AD. Some of

these genes and their relevance to AD are discussed below.

For instance,NISCHcodes for a cytosolic protein Nischarin which negatively

affects cell migration by forming inhibitory complexes with PAK family kinases

among other proteins [22]. PAK4 suppression decreases the phosphorylation of

LIMK1, key for axon/dendrite outgrowth and neuronal migration [23]. Another

gene,RABEP1 codes for rabaptin-1 which interacts with Gap-43. One of the main

roles of Gap-43 is adjustment of neurotransmitter release, endocytosis, and long-term

potentiation and its expression and function is altered in AD [24]. Recently identified

THEMIS produces a protein also known as GAB2 associated protein (Gasp) which

plays a crucial regulatory role in positive selection during thymocyte development

[25-27]. Post-positive selection, thymocytes differentiate into CD4 or CD8 single-

positive (SP) thymocytes as determined by their restriction to MHC class II and I

respectively. SP CD4 and CD8 in time leave the thymus for other organs and form

part of the adaptive immune system. It is thought Gasp may function through a new


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molecular pathway downstream of T-cell receptor (TCR) signalling [27]. Further

studies will be needed to establish the role this gene may play in the etiology of AD.

Changes of the expression of mitochondrial genes such asNDUFA12, part of

complex I, may alter the oxidative metabolism in AD [28, 29]. Complex I initiates

electron transfer by oxidizing NADH and transferring the electrons to coenzyme Q

while pumping protons across the mitochondrial membrane creating an electro-

chemical proton gradient [29]. The high rate of oxygen consumption needed for

normal function, polyunsaturated fatty acid and transition metal ion composition, and

limited antioxidant defence mechanisms renders neurons vulnerable to oxidative

damage [29, 30]. Energy decline and mitochondrial dysfunction are a major, early

event in AD. Complex I deficiency decreases energy production by oxidative

phosphorylation this in turn increases reactive oxygen species (ROS) which often

causes structural and functional cell membrane changes setting off a vicious cycle that

ends in apoptosis.

Research by Rhein et al found tau induces mitochondrial dysfunction and

increases levels of ROS and together with -amyloid synergistically alters complex I

function and energy balance with aging in AD [31]. Tau has specific sensitivity of

complex I oxidative phosphorylation system. Furthermore, -amyloid directly

interacts with mitochondria via the translocase of the outer membrane (TOM) system.

Additionally, maternal family history of AD links maternal inheritance of

mitochondria to predisposition to AD and glucose hypometabolism [31]. NDUFA12

is already listed as part of the AD pathway [32].

Approach II: Choice of SNPs with prior biological knowledge for model

building

Step 1


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Table 3 highlights 19 SNPs with the smallest p-values from the logistic regression and

stratified analysis, in strong LD, and within six genes potentially relevant to AD. The

LD patterns and gene overlays for the SNPs are shown in the supplementary

information (additional file 2, figure S1, additional file 3, figure S2, additional file 4,

figure S3, additional file 5, figure S4, additional file 6, figure S5, and additional file 7,

figure S6). Four genes, distinct from the ones already identified in approach I, are

discussed next.

ST5, suppression of tumorigenicity 5, encodes three proteins. One of the

proteins, p126, is an activator of mitogen-activated protein kinase MAPK1 also

known as ERK2 [33]. ERK1 and ERK2 are some of several proline-directed kinases

that have been shown to phosphorylate tau protein [34]. Tau binds and stabilizes

microtubules in cells and in neurons; intracellular transport occurs in axons through

microtubules [35]. Hyperphosphorylation reduces tau binding to microtubules and

may increase neurofibrillary tangles (NFTs) in cell bodies and dendrites of neurons

[36] . There is direct correlation between NFTs and memory decline in AD patients

[36]; however, it remains to be seen how important of a role ERK2 plays in the

hyperphosphorylation of tau. Another gene, TRPC1 codes for a TRP cation channel

protein expressed in the neurons of the hippocampus and cortex among other regions

of the brain. TRPC1 is activated by either G receptor proteins or intracellular Ca2+

depletion [37]. Strbing et al discovered that TRPC1 channels uniquely adjust

neuronal function independently of synapse processes [37]. In addition, they

demonstrated that TRPC1 can form heteromeric channels i.e. TRPC1/TRPC5.

TRPC5 is expressed in the hippocampus; TRPC1/TRPC5 is activated by Gq-coupled

receptors and not by Ca2+

depletion and its regulation is not neurotransmitter specific

[37]. Calcium signalling necessary for axonal regeneration in the adult CNS and for


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growth cone response of spinal neurons inXenopus to myelin-associated glycoprotein

is mediated by TRPC1 channels [38]. Calcium disequilibrium has been observed to

lead to neuronal injury and apoptosis [39]. Key modulators of calcium homeostasis

such as presenilins and CALHM1 have been associated with EOAD [39]; however,

there have not been prior studies on TRPC1 in AD patients.

ATG10 is an E2-like ligase protein involved in two ubiquitin-like

modifications essential for autophagosome formation [40]. Autophagy is an

intracellular degradation mechanism responsible for clearance of misfolded proteins,

pathogens, and organelles (organelles such as functionally disabled mitochondria in

aging) [41]. Double-membrane autophagosomes enclose cytoplasmic proteins and

later degrade them by fusing with lysosomes. Autophagy initiation enhances the

clearance of tau and offers a cytoprotective role. Overactive or dysfunctional

autophagy may promote neuronal cell death in disease states contributing to the

pathology of multiple neurodegenerative disorders [42]. Blocking autophagosome

formation by knockout of eitherATG5 orATG7genes causes ubiquitinated protein

aggregates and eventual neurodegeneration, demonstrating that autophagy is both

constitutive and essential for neuronal functioning [42].

A genome-wide screen study by Lipinski et al showed that ROS are common

mediators upstream of the activation of the type III PI3 kinase (critical protein in

autophagy initiation) in response to -amyloid peptide. On the other hand, lysosomal

blockage also caused by -amyloid is independent of ROS. Furthermore, they proved

that autophagy is transcriptionally down-regulated during normal aging in the human

brain in contrast to the autophagy up-regulation observed in later stages of AD human

brains. In addition, AD drugs they tested have inhibitory effects on autophagy,


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decreasing input into the lysosomal system; they hypothesized this may ameliorate

cellular stress in AD [43].

A fourth gene,ANO3 encodes anoctamin 3, rs1389421 is in a 49 kb LD region

upstream ofANO3 as seen in additional file 5, figure S4. The anoctamin family of ten

highly hydrophobic membrane proteins is also known as TMEM16[44]. Some

anoctamins function as Ca2+

activated Cl-channels (CaCCs) in the retinal

photoreceptor synaptic terminals and the olfactory sensory neurons; others participate

in tumor progression [44, 45]. Some studies indicate that olfactory neurogenesis

disruption is linked to AD [46]. ANO2, 3, and 4 are mostly expressed in neuronal

tissues. ANO3 andANO4 mRNA are equally expressed in spinal cord, brain stem,

cerebellum, and eye; however, it is not known how ano3 and ano4 function thus far

[44, 47]. Ist2p, ANO in S. cerevisiae, is translated locally at the peripheral

endoplasmic reticulum (ER) and may be inserted into the plasma membrane by the

fusion of peripheral ER with the plasma membrane. If these anion selective proteins

in mammals are transported by a similar novel mechanism, it is believed they might

have effects in protein synthesis in axons and dendrites [45].

Step 2

The 19 SNPs in LD with six genes that resulted from the analysis in step 1 above

were used next in RF. Table 4 shows that when combining the 19 SNPs with the

APOESNP the average 10-fold CV error rate is reduced to 20.5 %. The 10-fold CV

error rate is reduced to 16.9 % when the data set is the 19 SNPs,APOESNP, and 10

GAB2 SNPs, and using 600 trees and 11 features for tree building. The higher 10-fold

CV error rate obtained in approach II as compared to that of approach I may not be

due to LD. As Meng, Y.A. et al explain, in RF if a SNP is near the root of a tree in

the forest and a second SNP in LD with the first SNP is close to the leaf of the same


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tree, the permutation of the first SNP value will not increment the prediction error of

the tree because the second SNP can be a substitute for the first SNP. However, the

prediction error might be still somewhat increased [48].

ConclusionsIt is believed that LOAD is a complex disease caused by the interaction of multiple

genetic and environmental factors. In the past two years, at least eight genes have

been confirmed to be associated with LOAD. These are common risk variants of

moderate to small effects the same asAPOE. The new variants functionality could be

mapped to the immune response, cholesterol metabolism, and cell membrane

processes pathways [14]. However, a percentage of AD heritability is still missing.

The purpose of this study was to explore new associations between multiple SNPs and

AD by data mining approaches. We analyzed a published AD GWAS data set by a

couple of two-step approaches that first filtered the data with a low threshold to obtain

a data subset used in a second step for multi-locus analysis. In one approach

statistical and data mining techniques were implemented, and in the other approach

biological domain knowledge and LD analysis were done prior to the multi-locus

analysis. A 10-fold CV was done with the multi-locus analysis which helped remove

bias from the reported error rate. Previously AD associated SNPs [7, 9] were

removed from the data to avoid obscuring other possible significant variants. There is

overlap between the SNPs identified with both approaches; some of the genes

associated with the SNPs used to build the classifiers have not been reported before as

currently listed in the AlzGene database [10].

The model built for approach I confirmed, thatAPOEand GAB2 genotypes

alone can produce a moderate determinant of LOAD status [9], being able to


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discriminate between cases and controls with about 33 % error rate (10 fold CV). By

adding close to 200 other genome-wide SNPs that had a relatively high score of

association with LOAD from the GWAS data set, the error rate of the model was

greatly reduced, from 33 % to about 10 %. While many of the 200 SNPs were in the

vicinity of genes that could potentially be involved in AD pathways, some of them

were not.

The model built for approach II leveraged biological domain knowledge to

select a small number of SNPs from genes that had relevance to LOAD. This model

used only 19 to 30 SNPs (Table 4), while the model in approach I used one order of

magnitude more, about 200 SNPs (Table 2). The model in approach II was less

successful in lowering the LOAD classification error rate to only about 17 %, vs.

the 10% that the model in approach I did. Approach II; however, with its limited

number of biologically-relevant SNPs, would be much easier to test, as opposed to the

model in approach I, which included 200 genome-wide SNPs. A model employing

dozens of SNPs might be harder to test, but it could be that dozens of genetic variants

linked to many different pathways could be involved in the etiology of AD as is the

case for other complex diseases [19], and as it is beginning to emerge from the

GWAS outcomes from the past two years.

In order to improve the results from the analysis of this data, a (joint) meta-

analysis could be done with another AD data set conducted on the same platform.

The combined data sets would give more statistical power for gene-gene interaction

effects and make possible fine mapping of variants with larger effect sizes. The

functionality of the selected SNPs here could be further assessed by mapping the

variants to genes that interact with or are in the same pathways as those already


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implicated in AD, and querying of genomic annotations of SNPs representing

variation in microRNA target sites.

The two approaches described here are only a starting point that can be further

refined to better understand the possible causes of LOAD. Similar approaches that

combine high throughput genomics techniques, statistical and data mining analysis,

and leverage biological domain knowledge can be applied to study other complex

diseases that have a strong genetic component.

Methods

Data

A published AD GWAS data set was obtained from the Translational Genomics

Research Institute [9]. The data includes results on 312,316 SNPs that passed quality

control checks across the genome genotyped with the Mapping 500K Array set from

Affymetrix on 1411 LOAD cases and controls from a discovery group and two

replicate groups. Each of the three groups is divided into two sub-groups ofAPOE4

carriers and 4 non-carriers. In addition to genotypes, the original data includes

phenotypes such as gender, age of disease onset, and age at death. The analysis

presented here focuses on genotype interactions and excludes these phenotypes from

the analysis.

The data can be described as 312,316 nominal predictors along with a two

class response variable y. The response variable is unevenly distributed; it is 61 %

cases and 39 % controls. Also, the data has missing values. Furthermore, in order to

avoid false-positive results due to population stratification, the data is from a

Caucasian population of European ancestry; the samples were obtained from the

United States and from the Netherlands.


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The data was originally used to identify a novel interaction between LOAD

and two genes,APOEand GAB2 [9]. In this analysis, known GAB2 andAPOESNPs

are first excluded and then re-added in the model building phase. In the next section,

we describe the approaches employed and briefly explain the RF algorithm.

Analytical approaches for Alzheimers disease association analysis

In order to identify new genetic variants that increase disease risk, we implement

some of the latest algorithm versions for disease association, LD, and data mining

with the most recent genetic variant annotation files. A two step analysis, to reduce

multiple genetic interactions to be tested, is implemented by two approaches: one

statistically driven and a second incorporating sample stratification and biological

knowledge. In the first step of both approaches, SNPs are filtered at a less stringent

threshold for disease association. The multiple testing threshold correction,

Bonferroni, assumes there are M independent tests (p = e /M where p is the point-

wise error and e is the experimental error); however, the independence assumption

fails in genetic association studies since there is correlation among some of the SNPs

due to LD. Thus, we use a threshold of p-value = 1E-3 and take into account positive

ORs (a positive OR means the minor allele increases disease risk relative to the major

allele) for the genome-wide screening step. Furthermore, for the first step of

approach II, the data is also filtered by the p-values and ORs from chi-square tests,

and by significant LD values of selected SNPs within or close (~5 Kb) to

neurobiological relevant genes. For both two step methods, known GAB2 andAPOE

SNPs (originally published with this data set [9]) are first removed so they do not

obscure the finding of other statistically significant SNPs and then they are re-added

in the RF building phase.


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Random forest (or random forests) is an ensemble classifier that consists of

many decision trees and outputs the class that is the mode (most frequent outcome) of

the class's output by individual trees [49]. Ensemble methods use multiple models to

obtain better predictive performance than could be obtained from any of the

constituent models. For example, if individual classifiers would have an error rate of

= 0.35, an ensemble of twenty-five independent base classifiers will make a wrong

prediction at a smaller rate of 0.06 by the formula i(1-)

25-i= 0.06 [50].

RF is a special case of bagging algorithm which is simple to train and tune.

Bagging, or bootstrap aggregating, is a parallel ensemble method that induces

additional randomness by allowing bag size to be chosen [51]. For each classifier in

the ensemble, a sample is drawn uniformly and with replacement from the original

training data set. If the training data has more rows expressing cases than expressing

controls, the randomness causes more frequent cases rows in the bag than control

rows. This results in cases rows getting classified much better than the control rows.

The aim is to have both classes classified in a way to lead to overall low error rate.

RF, as a classifier, induces additional randomness in the selection of features from a

subset for deciding the splits at the nodes in each tree and if the skewed features in the

data are de-selected one may improve the model predictions or vice versa. The size of

the subset is decided by first taking the square root of the total number of attributes in

the data set or by the log2 of the total number of attributes + 1. The additional

randomness in RF helps to reduce variance (correlation among attributes) and

maintain bias. It is standard to let the trees grow deep and not to prune them since the

average of trees that are put together is taken to reduce the variance.


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In the second step for both approaches, RF algorithms are optimized to build

stable classifiers with new SNPs, the most significantAPOESNP (rs4420638)

identified by Coon et al [7], and 10 GAB2 SNPs (Table 1[9]) for AD prognosis.

Approach I: Choice of SNPs without prior biological knowledge for modelbuilding

Step 1

Logistic regression with a less stringent p-value than the Bonferroni cut-off of

0.05/312,316 = 1.55E-7 is performed using PLINK v 1.07 [52] on all samples. A

filter is set at a p-value = 1E-3 and the association analysis is run with removal of

APOEand GAB2 SNPs. In addition, SNPs with p-values < 1E-3 and 1 < ORs < 5 are

selected. The raw genotype data corresponding to each of the selected SNPs is

extracted by running Perl scripts and PLINK code. And to identify the genes

corresponding to these SNPs, annotation files updated by the chip manufacturer

(Affymetrix) with the Human Genome v 19 are queried.

Step 2

After pre-formatting the data subset, the data mining analysis is run using WEKA v

3-6-6 [53]. All the analysis in this study involves a cross validation of 10 folds

without pruning the trees. To start the RF model building, the number of SNPs to

include in the model is calculated by taking the square root of the total number of

SNPs. Four data sets are used to build the RF classifiers, and all of the sets have y

(disease) in the last column as class attribute. The first data set includes theAPOE

SNP and 10 GAB2 SNPs, the second set consists of the SNPs from step 1, the SNPs

from step 1 plus theAPOESNP form the third set, and the fourth set comprises the

SNPs from step 1, theAPOESNP, and 10 GAB2 SNPs. In order to assess the


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- 19 -

performance of each of the classifiers, the number of trees is held constant at 100 and

the number of features (SNPs) is varied. Then, the classifiers are tuned by holding

constant the various numbers of attributes, which gave the smallest test and OOB

error rates (for the three data sets) in the performance step, and by changing the

number of trees.

A supervised instance resample filter is applied to each data set. This

produces a random subsample of each data set using sampling with replacement. The

filter is set to bias the class distribution towards a uniform distribution; the original

case-control distribution in the data is 61 % cases and 39 % controls.

After classification modelling, a manual compilation of epistasis information

relevant to AD on the 199 SNPs is done.

Approach II: Choice of SNPs with prior biological knowledge for modelbuilding

Step 1

For the second approach, prior biological knowledge is used to supplement statistical

analysis in selecting SNPs from genes that are more likely to play a role in AD. SNPs

fromAPOEand GAB2 are excluded; then, the data is filtered by p-values < 1E-3 and

1 < ORs < 5 from logistic regression and the Cochran-Mantel-Haenszel (CMH) test.

For CMH, the stratification (three groups) is done based onAPOE4 carrier status

using PLINK.

A list of twenty-three SNPs selected from logistic regression and CMH based

on their p-values and ORs, and situated in the vicinity of genes potentially relevant to

AD is uploaded into Haploview v 4.2 [54] in order to find their linkage to other SNPs

within a 300 kb region. The default settings are kept; the Download HapMap info

Track, release 22 version 2 and release 21 with panel CEU (Caucasian European),

and the Solid Spine method to detect strong LD are utilized [54].


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Step 2

The RF is implemented at various fixed number of attributes and trees with four

different data sets. TheAPOESNP and 10 GAB2 SNPs are a first set, the SNPs in LD

from step 1 are a second set, and the SNPs from step 1 together with theAPOESNP

are a third set. The SNPs from step 1 added to theAPOESNP and GAB2 SNPs make

a fourth set. The classification building is performed in the same manner as for

approach I step 2.

Competing interestsThe authors declare that they have no competing interests.

Authors' contributions

Conceived and designed the experiments: VD. Analyzed the data and wrote the

paper: NB. Made major edits: VD. Both authors read and approved the manuscript.

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Figures

Figure 1 - RF performance assessment, different number of features andnumber of trees fixed at 100; approach I

Figure 2 - RF tuning, best number of attributes at different number of trees;approach I

F = number of features.


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Tables

Table 1 - Logistic regression top scoring SNPs, approach IGene Symbol

dbSNP RS ID

Distance to

Gene

Unadj.

p-value

FDR_BH

p-value

OR (95% CI) Pathway/Disease

/function

NISCH

rs6784615intron 7.16E-07 4.47E-02 2.21 (1.61-3.02) Interaction with

PAK4 for

reduction of

LIMK1

phosphorylation;

neuronalmigration and

axon/dendrite

outgrowth [22-

23].

RABEP1rs4356530

upstream27742

8.59E-07 4.47E-02 2.21 (1.61-3.02) Endocytosis [24,32].

THEMIS

rs9398855

intron 2.25E-06 8.78E-02 2.13 (1.56-2.92) Immnune

response [25-27].

NDUFA12rs249153

downstream

40719

4.13E-06 1.29E-01 1.62 (1.32-2.00) AD, Parkinson's,

Hungtinton's,

oxidative

phosphorylation

[28-31, 32].

MUC21rs2517509

downstream

72544

4.93E-06 1.39E-01 3.14 (1.92-5.12) Prevention of

cell-cellinteraction of

Integrins [55].

TUSC1rs10115381 upstream328744 5.33E-06 1.39E-01 2.08 (1.52-2.85) Shwachman-Diamond

syndrome [56].

CTNNA3

rs10996618downstream187123

1.19E-05 2.03E-01 2.03 (1.48-2.78) AD (4 studies),Adherens

junction, Inmune

response [10, 32].

FDR_BH = Benjamini & Hochberg (1995) step-up False Discovery Rate control.

Table 2 - RF modeling, filtered data, approach I

Data RF 10-Fold CV % Error

(APOE4+& 4-samples)

Number

of Trees

F=7 F =11 F =18 F=32 F=42

APOE & 10

GAB2 SNPs

100 33.3 33.2

300 33.3 33.2

600 33.4 33.5

199 SNPs

100 11.9 12.3 12.5 12.3

300 11.3 11.3 12.1 11.6

600 10.6 11.6 11.2 11.1


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APOE &

199 SNPs

100 10.9 9.9 10.4 10.3

300 8.9 9.6 9.5 10.3

600 9.0 9.1 10.1 9.9

APOE, 10GAB2, &

199 SNPs

100 9.9 10.1 9.9 11.2

300 8.9 9.1 9.5 10.3

600 9.1 9.4 9.9 10.3

F = number of features.

Table 3 - CMH top scoring SNPs in LD, approach II.

Gene (Chr.)

dbSNP RS ID

Physical

Position

Distance to

Gene

Minor

Allele

(MAF)

p-value

from 2

OR (95% CI)

APOE4-

SAMPLES

ST5 (11)

rs4910068 8830651 intron C (0.25) 9.47E-05 1.57 (1.25-1.98)

rs10743089 8744744 intron A (0.33) 2.12E-05 1.59 (1.28-1.97)

APOE4+

SAMPLES

TRPC1 (3)

rs4259003 144006245 intron A (0.21) 6.82E-05 2.36 (1.53-3.64)rs9784320 144024724 intron C (0.25) 9.68E-04 1.87 (1.28-2.71)

rs2033912 143999057 intron T (0.22) 1.71E-03 1.86 (1.26-2.75)

rs891159 81526843 intron C (0.24) 2.40E-05 2.34 (1.56-3.49)

ATG10 (5)

rs1485587 81362798 intron G (0.48) 8.11E-05 1.82 (1.35-2.45)

rs4703879 81589571 intron A (0.24) 1.01E-03 1.86 (1.28-2.71)

ANO3 (11)

rs1389421 25747721 upstream

561825

G (0.45) 3.39E-06 2.07 (1.52-2.83)

rs10834774 25715397 upstream

594149

C (0.20) 4.01E-03 1.86 (1.21-2.85)

rs11028909 25729021 upstream

580525

G (0.20) 4.13E-03 1.84 (1.21-2.81)

NDUFA12

(12)

rs249153 93848520 downstream

40719

C (0.19) 3.19E-06 1.63 (1.33-2.00)

rs249154 93848687 downstream

40552

C (0.18) 3.22E-05 1.54 (1.25-1.89)

APOE4+

and 4-

SAMPLES

NISCH(3)rs6784615 52481466 intron C (0.09) 6.63E-07 2.13 (1.57-2.88)


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- 32 -

rs9855470 52468315 intron A (0.06) 4.86E-05 2.11 (1.46-3.05)

rs6445486 52481531 intron A (0.06) 2.77E-04 1.90 (1.34-2.71)

rs10865972 52466487 intron C (0.06) 3.38E-04 1.87 (1.32-2.65)

rs4687619 52493826 intron T (0.06) 3.54E-04 1.93 (1.34-2.79)

rs6810027 52499614 intron C (0.05) 5.80E-04 1.89 (1.31-2.73)

All p-values are uncorrected.

Table 4 - RF modeling, filtered data, approach II.

Data RF 10-Fold Cross Validation % Error

(APOE4+ and

4- samples)

Number

of Trees

F = 6 F = 7 F = 11 F = 18

APOE & 10 GAB2

SNPs

100 33.3 33.2150 33.4 33.5

200 33.4 33.2

250 33.2 33.2

300 33.3 33.2

600 33.4 33.5

19 SNPs

100 26.9 27.5 27.9

150 26.7 27.0 27.5

200 26.8 27.0 27.4

250 27.1 27.3 27.6

300 27.2 27.1 27.6

600 27.1 27.4 27.4

APOE & 19 SNPs

100 20.3 20.8 20.7

150 19.9 20.6 20.8

200 20.1 20.7 20.9

250 20.3 20.6 20.6

300 20.1 20.6 20.6

600 19.8 20.4 20.7

APOE, 10 GAB2,

& 19 SNPs

100 17.6 17.6 18.2

150 17.2 17.0 17.6200 17.2 17.2 18.1

250 17.3 17.1 17.9

300 17.2 17.3 17.9

600 17.3 16.9 17.6

F = number of features


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Additional filesAdditional File 1 Table SA

Complete list of 199 SNPs from logistic regression approach I with corresponding

pathway, disease or biological function information which may be pertinent to AD.

Additional File 2 Figure S1

LD displayfor SNPs across the 300 kb region surrounding the ST5 locus. Top: Entrez

gene track overlaid with Hapmap genotyped SNPs across a 300 kb pairs interval

surrounding the ST5 locus. Bottom: zoomed LD SNP region.

The SNPs identified, rs4910068 and rs10743089, were found to be in significant LD

with a D value of 0.83. Standard color scheme for Haploview: D' < 1 and LOD < 2

are white, D = 1 and LOD < 2 are blue, D < 1 and LOD > 2 are shades of pink/red,

D = 1 and LOD > 2 are bright red. LOD = log of the odds.


LD displayfor SNPs across the 300 kb region surrounding the TRPC1 locus. Top:

Entrez gene track overlaid with Hapmap genotyped SNPs across a 300 kb region

surrounding the TRPC1 locus. Bottom two: zoomed LD SNP region.

The SNPs identified, rs4259003, rs9784320, and rs2033912, were found to be in

significant LD; rs4259003 and rs9784320, rs4259003 and rs2033912, and rs9784320

and rs2033912 with D values of 1.0. Standard color scheme for Haploview: D' < 1

and LOD < 2 are white, D = 1 and LOD < 2 are blue, D < 1 and LOD > 2 are shades

of pink/red, D = 1 and LOD > 2 are bright red. LOD = log of the odds.


LD displayfor SNPs across the 300 kb region surrounding theATG10 locus. Top:


surrounding theATG10 locus. Bottom three: zoomed LD SNP region. The SNPs

identified, rs891159, rs1485587, and rs4703879, were found to be in significant LD;

rs891159 and rs1485587, rs891159 and rs4703879, and rs1485587 and rs4703879

with D = 1. Standard color scheme for Haploview: D' < 1 and LOD < 2 are white, D


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= 1 and LOD < 2 are blue, D < 1 and LOD > 2 are shades of pink/red, D = 1 and

LOD > 2 are bright red. LOD = log of the odds.


LD display

for SNPs across the 300 kb region surrounding theANO3 locus. Top:


surrounding theANO3 locus. Bottom two: zoomed LD SNP region. The SNPs

identified, rs1389421, rs10834774, and rs11028909, were found to be in significant

LD; rs1389421 and rs10834774, rs1389421 and rs10834774, and rs11028909 and

rs10834774 with D = 1. Standard color scheme for Haploview: D' < 1 and LOD < 2

are white, D = 1 and LOD < 2 are blue, D < 1 and LOD > 2 are shades of pink/red,

D = 1 and LOD > 2 are bright red. LOD = log of the odds.


LD displayfor SNPs across the 300 kb region surrounding theNDUFA12 locus. Top:

Entrez gene track overlaid with Hapmap genotyped SNPs across a 300 kb pairs

interval surrounding theNDUFA12 locus. Bottom: zoomed LD SNP region. The

SNPs identified, rs249153 and rs249154, were found to be in significant LD with D =

1. Standard color scheme for Haploview: D' < 1 and LOD < 2 are white, D = 1 and

LOD < 2 are blue, D < 1 and LOD > 2 are shades of pink/red, D = 1 and LOD > 2

are bright red. LOD = log of the odds.


LD display

for SNPs across the 300 kb region surrounding theNISCHlocus. Top:


surrounding theNISCHlocus. Bottom: zoomed LD SNP region. The SNPs identified,

rs6784615, rs9855470, rs6445486, rs10865972, rs4687619, and rs6810027, were

found to be in significant LD; rs6784615 and rs9855470, rs6784615 and rs6445486,

rs6784615 and rs10865972, rs6784615 and rs4687619, rs6784615 and rs6810027,



36/39

- 35 -



and rs4687619 and rs6810027 with D values of 1. Standard color scheme for

Haploview: D' < 1 and LOD < 2 are white, D = 1 and LOD < 2 are blue, D < 1 and

LOD > 2 are shades of pink/red, D = 1 and LOD > 2 are bright red. LOD = log of

the odds.


37/39

gure 1


38/39

gure 2


39/39

Additional files provided with this submission:

Additional file 1: Additional_file1_Table_SA.xlsx, 80Khttp://www.biomedcentral.com/imedia/2053611851577277/supp1.xlsxAdditional file 2: Additional_file2_Figure_S1.pdf, 309K

http://www.biomedcentral.com/imedia/5310161575772777/supp2.pdfAdditional file 3: Additional_file3_Figure_S2.pdf, 246Khttp://www.biomedcentral.com/imedia/1079060595577277/supp3.pdfAdditional file 4: Additional_file4_Figure_S3.pdf, 428Khttp://www.biomedcentral.com/imedia/6991932595772777/supp4.pdfAdditional file 5: Additional_file5_Figure_S4.pdf, 453Khttp://www.biomedcentral.com/imedia/1393643957577277/supp5.pdfAdditional file 6: Additional_file6_Figure_S5.pdf, 242Khttp://www.biomedcentral.com/imedia/2564752965772777/supp6.pdfAdditional file 7: Additional_file7_Figure_S6.pdf, 297Khttp://www.biomedcentral.com/imedia/1349460482577277/supp7.pdf
http://www.biomedcentral.com/imedia/2053611851577277/supp1.xlsxhttp://www.biomedcentral.com/imedia/2053611851577277/supp1.xlsx

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